Typically, a telecommunication network is shared by many users. The resources of the network (e.g., cell number/size, transmission bandwidth and routing/switching capacity) are engineered to handle a given demand with a given quality of service. The given demand is typically based on an “average user,” the behaviour of which is constructed from a mix of assumptions and measurements. Many tariffs (e.g., flat rates) are set to match the resources consumed by the average user.
The statistical methods used for dimensioning networks and setting tariffs work well as long as user behaviour is relatively homogeneous. If this is not the case, then the result may be congested networks with poor performance and unbalanced tariffs where less active users in effect subsidise more active users.
Several measurements have shown that the degree of activity varies considerably between different users. In more detail, the vast majority of users exhibit low or medium activity while a small number of users exhibit high or extreme activity. The users exhibiting high or extreme activity (a.k.a., “heavy hitters”) pose a potential problem to network operators because such users tend to offset the dimensioning model by flooding the network and creating traffic peaks at the expense of the other users. In addition to the notion of “heavy hitters,” there is the notion of “bad applications,” which (at present) typically include file sharing applications that use peer-to-peer protocols. The term “heavy hitter” is often used in this context as well.
The root of the problem presented by “heavy hitters” is the flat rate tariff scheme. An obvious solution is thus to charge each user by the amount of network traffic the user generates, in the same way as telephone use is charged by time. The problem with this solution, however, is that traffic volumes are hard to understand for ordinary users. Hence, the result is an uncertainty about costs which results in ordinary users tending to refrain from using the service at all.
Another option is to impose some sort of upper limit on traffic volumes. Such a limit can easily be chosen such that most ordinary users never will hit this limit. The issue with this scheme is what to do with the users that do hit the limit. One option is to make contact with such users, discuss their usage and offer different upgrades to their subscriptions. A difficulty with this approach, besides the fact that it requires manual intervention, is that users may perceive such contacts as a threat to their privacy. A non-manual option is to simply reject excess volumes, but this solution would most likely be perceived as too hostile to customers. A more advanced non-manual option is to automatically apply additional charges for excess volumes, but this solution leads back to the uncertainty problem which the flat rate scheme originally was devised to avoid.
What is desired are systems and methods for overcoming at least some of the above described problems.